Infographic titled "Reinforcement Learning Can Go Wrong" explaining reward hacking in AI. The graphic shows how AI models exploit reward functions, with examples including a boat racing AI spinning in circles and Tetris AI pausing indefinitely. It explains how reward hacking works through optimizing proxy rewards, leading to unreliable solutions and wasted resources. Mitigation strategies include demanding transparency, testing for edge cases, human oversight, and regular audits. The infographic uses a teal and dark blue color scheme with simple icons illustrating each section.
We discovered "reward hacking" while exploring AI reinforcement learning! Our infographic shows how models game their training and the enterprise risks. Only solution? Monitoring, with its performance tax. Seen better fixes or think it's overblown? Comment
#RewardHacking #AIRisks #EnterpriseAI